Lab 2

# Rmarkdown to docx instructions
# https://rmarkdown.rstudio.com/articles_docx.html
# https://bookdown.org/yihui/rmarkdown/word-document.html

# R-Markdown: The Definitive Guide
# https://bookdown.org/yihui/rmarkdown/

9-15-21 Data collection in field Setup at organic farm at 11:54am. Site chosen near apple trees.

Station 2, .1L / 30m. 12:05pm, 12:35pm, 1:05pm applications Station 3, .5L / 30m. 12:00pm, 12:30pm, 1:00pm applications

df_names <- c("TIMESTAMP", "RECORD", "BattV_Avg", "PTemp_C_Avg", "VWC_Avg", "EC_Avg", "T_Avg", "P_Avg", "PA_Avg", "VR_Avg", "station", "water_L")

# Station 2, .1L / 30m. 12:05pm, 12:35pm, 1:05pm applications
# was cleared before collection
station2_df <- read_csv("Lab2_data/CR300Series_2_Table1 - Copy.dat", skip=6, col_names=FALSE) %>%
  mutate(station = 2, water_L = .1)

## Rows: 19 Columns: 10

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl  (9): X2, X3, X4, X5, X6, X7, X8, X9, X10
## dttm (1): X1

## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

# Station 3, .5L / 30m. 12:00pm, 12:30pm, 1:00pm applications
# station 3 had data from previous runs on it, needs to be filtered out
station3_df <-read_csv("Lab2_data/CR300Series_3_Table1 - Copy.dat", skip=610, col_names=FALSE) %>%
  mutate(station = 3, water_L = .5)

## Rows: 21 Columns: 10

## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl  (9): X2, X3, X4, X5, X6, X7, X8, X9, X10
## dttm (1): X1

## 
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.

# names of columns
names(station2_df) <- df_names
names(station3_df) <- df_names

# combine them
my_df <- bind_rows(station2_df, station3_df)

Topic: Integration of CR310 Data Logger with soil sensors with data logger for experimentation and wireless data acquisition

Field experiment set-up: * configured

# Flow Chart in R using DiagrammeR package, docs: https://rich-iannone.github.io/DiagrammeR/docs.html

# create a flow chart
grViz(diagram = "digraph dot {
  graph [layout = dot]

  # define node aesthetics
  node [fontname = arial, 
        shape = oval, 
        color = gray, 
        style = filled, 
        fontcolor = White, 
        fontsize = 11]
        
  tab1 [label = '@@1']
  tab2 [label = '@@2']
  tab3 [label = '@@3']
  tab4 [label = '@@4']
  tab5 [label = '@@5']
  tab6 [label = '@@6']
  
  # set up node layout
  tab1 -> tab2
  tab2 -> tab3
  tab2 -> tab6
  tab6 -> tab2
  tab6 -> tab4
  tab4 -> tab5
  tab5 -> tab1
  tab3 -> tab1
  }
  
  # define tab labels
  [1]: 'Learning Data Science to better fight a walrus'
  [2]: 'fighting a walrus'
  [3]: 'lose the fight'
  [4]: 'laser swords run out of batteries'
  [5]: 'go to store and get batteries'
  [6]: 'win a laser sword as loot'
  
  ")

# DiagrammeR mermaid graph

mermaid("
graph LR
  A(Learning Data Science to better fight a walrus)-->B
  A-->C[lose the fight]
  C-->A
  C-->E(taco break)
  B[fighting a walrus]-->D{laser swords run out of batteries}
  C-->D(go to store and get batteries)
  D-->F
  E-->F{win a laser sword as loot}
")

mermaid("graph.mmd")

# Sequence Diagrams, as seen in "How to Draw Sequence Diagrams" report by Poranen, Makinen, and Nummenmaa 
# offers a good introduction to sequence diagrams. 
#Let's replicate the ticket-buying example from Figure 1 of this report and add in some conditionals.

mermaid("
sequenceDiagram
  customer->>ticket seller: ask ticket
  ticket seller->>database: seats
  alt tickets available
    database->>ticket seller: ok
    ticket seller->>customer: confirm
    customer->>ticket seller: ok
    ticket seller->>database: book a seat
    ticket seller->>printer: print ticket
  else sold out
    database->>ticket seller: none left
    ticket seller->>customer: sorry
  end
")

mermaid("graph.mmd")